⚡ PyOPF: Optimal Power Flow Modeling in Python

PyOPF is Optimal Power Flow (OPF) modeling framework in Python. This modeling is basically based on Pyomo, which is a solver-agnostic optimization modeling package in Python. PyOPF generally can take PGLib based input and formulate various OPF problems including AC-OPF, DC-OPF.

Installation

pip install opf

Dependencies
- python>=3.8
- pyomo>=6.5.0
- numpy>=1.22.3
- ipopt>=1.0.3

Formulations

⭕ AC-OPF (AC Optimal Power Flow):
```
model = opf.build_model('acopf')
```
- AC-OPF with a polar bus voltage variable representations.
- The detailed formulation can be found in PGLib.
- PyOPF takes the the input files from PGLib, which is basically based on MATPOWER format.
- Uses various solvers supported in Pyomo including IPOPT and Gurobi to solve problem instances.
⭕ DC-OPF (DC Optimal Power Flow)
```
model = opf.build_model('dcopf')      # base DC-OPF model
model = opf.build_model('dcopf-ptdf') # DC-OPF model using PTDF
```
- Linear approximation to AC-OPF.
- Also support PTDF (power transfer distribution factor) based formulation.
- Only use active power generations and bus voltage angles (for base DC-OPF) as variables.
- Like AC-OPF, PGLib m-files can be taken as input.

Warmstarting

PyOPF fully supports primal and dual warmstarting for IPOPT. Documentation is to be added.
```
model.setup_warmstart(warmstart_solution_dict) 
```

Examples

Running AC-OPF from PGLib.

Before solving the AC-OPF, you should install IPOPT, which is a canonical solver for AC-OPF, as follows:
```
conda install -c conda-forge ipopt
```

Running the following AC-OPF problem

import opf

# build abstract model for AC-OPF
model = opf.build_model('acopf')

# load pglib input model file
network = opf.parse_file("./data/pglib_opf_case5_pjm.m")

# create the model instance (concrete model)
model.instantiate(network)

# solve the problem
result = model.solve(solver_option={'print_level' : 5, 'linear_solver': 'ma27'}, tee=True)

# check the optimal objective value
print('obj value', result['obj_cost'])

# check the (primal) optimal solution
print('primal solution', result['sol']['primal'])

Citation

If you exploit this repository in your research, please cite using the following BibTeX:

@software{
    PyOPF_2023,
    author = {Park, Seonho},
    doi = {10.5281/zenodo.7772531},
    license = {MIT},
    month = {10},
    title = {{PyOPF: Optimal Power Flow Modeling in Python}},
    version = {0.3.1},
    year = {2023}
}

yasirroni / PyOPF